u/bonobo65k

Hi everyone.

I work at a startup and we recently released a product focused on swarm agents/AI/decision making.

The product is already online and working (in beta), but on the team, we're all dev/financial oriented... we're missing that person who can guide initial users toward our idea.

Equity, no salary. The only requirement is to have been on Reddit for a few years, understand user behavior, be astute, and, above all, active in multiple subreddits.

Hy everyone! I need some beta testers for my tool (completely free):

Kith - https://kith.software

I will be very happy to try your tools in return and to create organic engagement, just drop your links in the comments!!

Honest question.

The more complex AI systems I work on, the more I wonder whether I’m:

- solving the wrong problem
- adding structure where judgment would be enough

Multi-step reasoning, agent debates, memory, synthesis — all interesting, but also heavy.
For those of you building serious AI tools:
how do you tell when complexity is justified?
when do you stop and say “this should be simpler”?

I don’t have a good answer yet.

ciao a tutti startuppers! Ho rilasciato un progetto a cui ho lavorato ultimamente…ma senza P.IVA come consigliate di monetizzare? Paddle/LemonSqueezy e affini comunque te la richiedono per tirare fuori i soldi…..

Attualmente ho rilasciato il software in private beta gratuita quindi se qualcuno ha piacere di darmi qualche feedback sarebbe più che gradito!

I’ve been experimenting for a while with LLM-based agents and orchestration frameworks, and I keep running into the same issue:
they look impressive on paper, but when it comes to real problem solving they often feel fragile, hard to adapt, or too abstract.

What I’m trying to understand is:

- how do you make agents actually learn from usage?
- how do you keep outputs understandable instead of “AI soup”?
- how do you avoid building something that only works in demos?

I’m exploring a personal project around these questions, but before going further I’d really like to hear how others are approaching this.
If you’ve worked with agent systems, councils, or orchestration setups, I’d love to hear what didn’t work for you.

I’ve been experimenting for a while with LLM-based agents and orchestration frameworks, and I keep running into the same issue:
they look impressive on paper, but when it comes to real problem solving they often feel fragile, hard to adapt, or too abstract.

What I’m trying to understand is:

- how do you make agents actually learn from usage?
- how do you keep outputs understandable instead of “AI soup”?
- how do you avoid building something that only works in demos?

I’m exploring a personal project around these questions, but before going further I’d really like to hear how others are approaching this.
If you’ve worked with agent systems, councils, or orchestration setups, I’d love to hear what didn’t work for you.